heavy duty gas turbine monitoring based on adaptive neuro

IMPACT CASE STUDY Open Access

Heavy duty gas turbine monitoring basedon adaptive neuro-fuzzy inference system:speed and exhaust temperature controlNadji Hadroug1, Ahmed Hafaifa1*, Mouloud Guemana2, Abdellah Kouzou1, Abudura Salam2 and Ahmed Chaibet3

* Correspondence:[email protected] Automation and IndustrialDiagnostics Laboratory, Faculty ofScience and Technology, Universityof Djelfa, 17000 Djelfa, DZ, AlgeriaFull list of author information isavailable at the end of the article

Abstract

Gas turbines are currently a popular power generation technology in countries withaccess to natural gas resources. However they are very complex systems the operationof which at peak performance is challenging. This paper proposes the use of a hybridapproach based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the controlof the speed and the exhaust temperature of a gas turbine. The main aim is tomaintain turbine operation at optimum performance. The results obtained, basedon the use of the Rowen model, clearly show the effectiveness of the proposedhybrid speed/exhaust temperature control approach for the gas turbine.

Keywords: Adaptive neuro-fuzzy inference system (ANFIS), Gas turbine,Exploitation data, Exhaust temperature, Rowen model, Heavy duty gas turbine (HDGT),Hybrid learning

BackgroundIn recent years, gas turbines have become important and widespread devices for heavy

industrial applications including electrical power generation and service in the oil and

gas industries. Keeping these turbines operating at optimal efficiency is an important

research question for the manufacturer and operator of these devices. Turbines are

very complex systems that require advanced control techniques to ensure the proper

control of their operating parameters. Of particular interest is the control of the speed

and the exhaust temperature. Note that the control of the exhaust temperature is

affected by ambient environmental parameters.

Recently several studies have been performed to ensure the modelling and the con-

trol of gas turbine. Benyounes et al. have proposed a fuzzy logic approach to modelling

and controlling vibrations in gas turbines used for pipeline gas transportation [1, 2].

Balamurugan et al. have studied the control of the load frequency of an operating gas

turbine plant based on signal processing analysis, via both large and small signal

models [3]. Asgari et al. introduced the Nonlinear Autoregressive Exogeneous (NARX)

model for the simulation of single shaft gas turbine startup operation [4]. Zaidan et al.

have proposed a prognostics system to predict gas turbine behavior using a Bayesian

hierarchical model based on a variational approach [5]. Zhu et al. developed a math-

ematical model to study steam turbine operation phases based on an optimization

Mathematics-in-IndustryCase Studies

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Hadroug et al. Mathematics-in-Industry Case Studies (2017) 8:8 DOI 10.1186/s40929-017-0017-8

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approach [6] and Onabanjo et al. have modelled the degradation of gas turbine

components [7].

The development of gas turbine control systems is important in the oil and gas

industries, where turbines are used both in power generation and pipeline gas transpor-

tation plants [2, 8–17]. In this context an adaptive neuro-fuzzy inference system

(ANFIS)- based hybrid control approach is proposed in this paper. The main objective

of the ANFIS control is to ensure the proper control of the speed and the exhaust

temperature of a gas turbine based on the Rowen model [18]. It is important to note

that the control system proposed in this paper is able to cope with changes in environ-

mental parameters such as ambient temperature, not only by reducing the controlled

parameter errors, but also by improving the quality of the response time and reducing

the maximum overshoot of the proposed control system.

In 1983 Rowen developed a model of a heavy duty gas turbine plant based on a trans-

fer function block diagram. His main idea was to build a simulation model to aid in the

control of three main parameters of the gas turbine: the speed, the exhaust

temperature, and the acceleration. Rowen succeeded in validating the system gains

using coefficients and time constants estimated for his model based on test and field

experience accumulated from numerous installations for many different applications

[6]. The decisions taken during the control of a gas turbine profoundly impact the

operating cost of the installation. The gas turbine system is a complex nonlinear system

featuring strong interactions between operating variables, so it is important to account

for the impact of monitoring system behavior on the system behavior. For this reason

traditional approaches to turbine control are less effective at ensuring the required,

reliable, level of control of parameter dynamic behavior.In this study, a dynamical

analysis of the control of a representative gas turbine is carried out based on real oper-

ating conditions. The proposed neuro-fuzzy control approach is implemented by com-

bining a neural network approach and a fuzzy systems approach in a homogeneous

architecture. This paper demonstrates that the proposed hybrid approach improves the

control of both speed and gas exhaust temperature for the gas turbine considered.

Case descriptionSeveral models have been developed in many applications for the control of the

dynamic behavior of gas turbines. However, the complexity of the dynamic behavior of

the gas turbine systems increases the difficulty of obtaining a reliable monitoring

system for such devices.

This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS)

approach to ensure the control of the speed and the exhaust temperature of a gas

turbine based on Rowen model [18], in order to maintain its optimum performance.

Gas turbine modeling

The present paper deals with a double shaft heavy duty gas turbine (HDGT) composed

of two parts: the double-shaft rotor and the fuel control system. The fuel control

system has four main functions: speed control, temperature control, throttle control, and

the control of max (upper) and min (lower) fuel limits. The speed control mechanism is

suitable for static applications or isochronous (time invariant) controls, and works

Hadroug et al. Mathematics-in-Industry Case Studies (2017) 8:8 of 20

sufficiently well over some dynamic speed range. The representation of the HDGT studied

here is based on the Rowen model [6]. In the Rowen model the control system is based

on three typical control loops: a speed control loop, a temperature control loop, and an

acceleration control loop [9, 11–13, 18]. The main data of a representative HDGT turbine

studied here are assembled in Table 1.

The proposed control configuration is shown in Fig. 1. The closed loop strategy

allows the system performance to be optimized at a nominal operating point of the

representative turbine [19, 12]. The real process (blue curve) and the theoretical

process (red curve) of the T-S diagram are presented in Fig. 2.

When the post-compression temperature inside the compressor follows an isentropic

process, this temperature, at the nominal operating point, can be calculated as follows:

T2s ocð Þ ¼ T 1 ocð Þ � Za ocð Þ ¼ 300:3� 11:3� 537:3539

� �0:285

¼ 598:81K ¼ 325:81�C ð1Þ

The index denoted by s corresponds to variables for the theoretical isentropic

process, the index “oc” describes variables measured at operating conditions. T1(oc) pre-

sents the ambient temperature at the operating conditions in kelvin (K), T2s(oc) presents

the theoretical inside compressor temperature and Za(oc) is a factor used to simplify cal-

culations related to the operating conditions. This factor is defined as:

Za ocð Þ ¼ PR � _mt

_mn

� �γa−1γa ð2Þ

where: γa is the specific heat ratio which is defined as γa = Cpa/(CPa − 0.287) = 1.4,Cpa is

the specific heat at constant air pressure (Cpa = 1.015 ),PR = 11.3 is the compressor

pressure ratio which is given in Table 2. _mt ¼ 537:3kg=s is the typical exhaust mass

flow and _mn ¼ 539kg=s is the nominal exhaust mass flow.

The real inside compressor temperature T2 can be calculated as follows:

T2 ¼ T 1 1þ Za−1ηc

c

� �¼ 598:7K ð3Þ

Here, T1 is the real ambient temperature and ηc is the efficiency of the compressor

which can be calculated as follows:

ηc ¼T 2s ocð Þ−T 1

T 2 ocð Þ−T 1¼ 325:81−27:3

401:54−27:3¼ 0:79

and

Table 1 The data of heavy duty gas turbines

The metric Units

Model 7001B

The speed of turbine 3000 rpm

Nominal-Temperature 510 °C

Max torque 16.231 Kg.M

Inertia 7.834 Kg.M2

Power Rating 157.7 MW


Za ¼ Pγa−1γa

� �R ¼ 11:3

1:4−11:4ð Þ ¼ 2

When the turbine temperature follows an isentropic process, the theoretical exhaust

temperature of the gas turbine (HP) section at the operating conditions can be calcu-

lated as follows:

T4s ocð Þ ¼T 3 ocð ÞZg ocð Þ

¼ 1333

11:3� 537:3539

� �0:24 ¼ 744:69k ¼ 471:69∘C ð4Þ

Here, T3(oc) is the combustion chamber temperature at the operating conditions and

Za(oc) is a factor used to simplify the calculations defined as:

control system

T4

Speed control system

Combustion chamber

The speed of high pressure

shaftThe speed of low pressure

shaft

Average error

Mass flow of air

Air mass flow compressor

T4 : The exhaust temperature

Fig. 1 Control setting of different subsystem of the TITAN 130 turbine

Entropy (KJ/Kg.K)0 0.2 0.4 0.6 0.8 1 1.2 1.4

Tem

pera

ture

(K

)

0

200

400

600

800

1000

1200

1400

1

2

3

4

4s

Gas turbine (Simple cycle)

2s

Fig. 2 T-S diagram of the real process in the studied gas turbine


Zg ocð Þ ¼ PR � _mt

_mn

� �γg−1

γg ð5Þ

The real exhaust temperature of the gas turbine will be very high and it is calculated

as follows:

T4 ¼ T 3 1−ηt 1−1Zg

� �� ¼ 770:55k ¼ 497:56�C ð6Þ

Where T3 is the combustion chamber temperature, T4is the temperature of the

exhaust gas of the gas turbine (HP) section and γg = 1.333 is the specific heat ratio of

the gas.

with: ηt ¼ T 3−T4 ocð ÞT 3−T 4s ocð Þ

¼ 1060−5101060−471:69 ¼ 0:935 and Zg ¼ R

γg−1

γg

� �¼ 11:3

1:33−11:33ð Þ ¼ 1:824.

The compressor input power is given by the following equation:

Power ¼ _m � Cpa T 2−T 1ð Þ ð7Þ

Where _ma is the air mass flow, _mg is the gas mass flow, Cpg is the specific heat at

constant gas pressure, Cpa is the specific heat at constant air pressure, with Cpa = 1.015

and Cpg = 1.149.

And the output power of high pressure turbine HP is given by the following

expression:

Power ¼ _m � Cpg T3−T 4ð Þ ð8Þ

The torque per unit of the generated mechanical power of the studied gas turbine is

presented as follows:

PG ¼ _m � Cpg T 3−T 4ð Þ−Cpa T 2−T 1ð Þ� ð9Þ

The Rowen model assumes a linear relationship with rotational speed within a

velocity band from 95% to 107% of the nominal speed. In this region the unitary output

power is calculated with:

Table 2 Turbine parameters in nominal operating conditions [22]

Parameter Symbol Unit Value

Compressor pressure ratio PR / 11.3

Fuel mass flow / kg/s 9.1

Lower heating value of fuel LHV kJ/kg 42,532

Exhaust mass flow _mn kg/s 539

Exhaust temperature TR °C 510

Nominal frequency F Hz 50-60

Electrical power PGn MW 157.7

The inside compressor temperature T2(oc) °C 401.54

Efficiency (simple cycle) η % 34.7

Turbine speed N rpm 3000


PGP⋅U ¼ PGn calculated mechanical powerð ÞPGn Electrical powerð Þ

PGP⋅U ¼ _mn � Cpg T 3−T4ð Þ−Cpa T 2−T 1ð Þ� PGn Electrical powerð Þ

ð10Þ

So based on eqs. (1), (3) and (10), the output power output per unit can be calculated

as follows:

PGP⋅U ¼_mn � T1 1− 1

Zg

� �Cpg ⋅ηt 1þ Za−1

ηc

� �h i−Cpa

Za−1ηc

� �n oþ ηcomb⋅ηtH⋅ _mfn tð Þ 1− 1

Zg

� �PGn Electrical powerð Þ

ð11Þ

with

_mfn tð Þ ¼ _mfn⋅ _mfp⋅u

Finally, the output power per unit is given as:

PGP⋅U ¼ Aþ B⋅ _mfp⋅u ð12Þ

The turbine parameters are calculated for nominal operating conditions according to

Table 2.

It is obvious that when the HDGT is operating at nominal speed, the output torque

(p.u) and the mechanical power (PG) are almost the same (A ¼ −0:223; B ¼ 1:221;

_mfn ¼ 5:129kg=s).

Fuel valve positioner system modeling

The valve positioner in the HDGT moves the actuator to a valve position which corre-

sponds to the reference position value. The principle of the studied gas turbine valve

positioner is shown in Fig. 3.

Based the Rowen model, the fuel flow equations are expressed as follows:

Valve Positioner

Valve Actuator

Pneumatic connections

Travel detection signal

Fuel pipe Fuel valve

Fig. 3 The valve positioner of the studied HDGT


_W f ¼ K3Pv−Wf

t3ð13Þ

Here, Pv is the fuel per unit consumed in the combustion chamber, given by the

expression:

_Pv ¼ −1t2Pvþ 1−KNLð ÞK2

t2VCE þ K2

t2KNL−

K 2K 4

t2Wf ð14Þ

This representation facilitates the modeling of the studied gas turbine system, it al-

lows a direct processing of the control variables of the HDGT, with a reliable configur-

ation system, as shown in Figs. 4 and 5. The differential equations of the associated gas

turbine, written in the Laplace transform domain, can be expressed as follows:

SPv sð ÞWf sð Þ

�¼

−1t2

K3

t3

−K2K4

t2

−1t3

26664

37775

Pv sð ÞWf sð Þ

�þ

1−KNLð Þt2

0

K2

t20

24

35 VCE

KNL

�ð15Þ

Where;

s:p sð Þ is the Labplace transform ofdp tð Þdt

¼ _p tð Þ

s:wf sð Þ is the Labplace transform ofdwf tð Þdt

¼ _wf tð Þ��→

8>>>><>>>>:

p(t) and wf(t) are functions of (t) (i.e., functions of time domain), p(s) and wf(s) are

functions of (s) (i.e., functions of frequency domain).

The PID controller is used to ensure the control of the installed gas turbine in oil

and gas plants, where they are used to ensure the most important gas turbine parame-

ters, especially speed and exhaust temperature, remain within desired bounds. While

PID controllers are very simple, they are based on system linearization and so suffer

from the major drawback of being non robust for nonlinear systems, with the control

constants Ki, Kp, and Kd changing dramatically as process parameters change. To over-

come this drawback, the ANFIS technique suggested in this paper may easily and

Fig. 4 Simplified model of the gas turbine speed control


rapidly be updated as gas turbine parameters and ambient environmental variables

change. The optimization considers the performance of the entire process.

Adaptive neuro-fuzzy inference system (ANFIS)

The principle of fuzzy approaches in the sense that the variables are not treated as lo-

gical variables but as linguistic variables close to human language. Furthermore, these

linguistic variables are processed using rules that refer to some knowledge of the sys-

tem behavior [18].

A whole series of fundamental concepts are developed in fuzzy logic. These concepts

are used to justify and demonstrate the basic principles. The structure of a conventional

fuzzy controller is shown in Fig. 6, it is composed of four separate blocks whose defini-

tions are given below. The fuzzy controller is designed to automatically run a process

based on a set by acting on the control variables, and it has some characteristics that

are intrinsic advantages.

A rule-base (a set of IF-THEN rules) contains a fuzzy logic quantification of the lin-

guistic description of the expert for how to achieve good control. An inference mech-

anism (also called “inference engine”) emulates the interpretation and application of

Fig. 5 Simplified model of the gas turbine temperature control

Input

Fno

itac

ifizz

u Fuzzy inference Doit

acifi

zzuf

e

Base regales

Output

Fuzzy controller

Process

Fig. 6 Implementation of a fuzzy controller


knowledge on how best to control the plant. A fuzzification interface converts the

information into control signals for the inference mechanism which can be used to

activate and apply rules. A defuzzification interface converts the results of the inference

mechanism into the real process inputs. The rule-base is constructed so that a human

expert is “in the loop.” The information in the rules of the rule-base can come from a

real human expert who has spent a long time learning the best way to control the

process. The fuzzification process plays an important role in the relationship between

the soft information that can be objective or subjective.

In this work, the adaptive neuro-fuzzy inference system (ANFIS) approach is used for

the speed and the exhaust temperature control of the gas turbine, where an automatic

model for the fuzzy rule generation is use. This mode is based on the inference model

of Takagi Sugeno which was proposed by JSR Jang on 1993 [6]. Indeed, the ANFIS

approach has attracted more industrial attention and it has been applied in several

industrial applications [6, 7, 12–18, 20, 21]. Based on the ANFIS structure is used to

ensure the control of the gas turbine instead of the classical controllers. The ANFIS ap-

proach is now well known and as it was well explained in detail in the previous works,

it is basically composed of five neuronal layers that refine the fuzzy rules established by

human experts and adjust the overlap between fuzzy sets, to describe the input-output

behavior of the presented gas turbine system [6, 7, 12–18, 20, 21]. On the other side, to

use the basic architecture of ANFIS model a fuzzy inference system of Sugeno first

order type is considered and two input linguistic variables x1 and x2 and one output y

are supposed. Furthermore, the basic rules are assumed to be of two broad types:

R1 : If x1 is A1 and x2 is B1 Then y1 ¼ f 1 x; yð Þ ¼ p1xþ q1yþ r1R2 : If x1 is A2 and x2 is B2 Then y2 ¼ f 2 x; yð Þ ¼ p2xþ q2yþ r2

ð16Þ

Where x1 and x2 are the inputs, A1 and B2 are the fuzzy sets, y1 and y2 are the out-

puts of all defuzzification of neurons, pi, ri and qi are the parameters of the ith rule de-

termined during the learning process.

The structure of the proposed adaptive neuro-fuzzy network is shown in Fig. 7.

The outputs of the first layer are presenting the degrees of membership of the input

variables x1 and x2:

Oi1 ¼ μAi

xð Þ; i ¼ 1; 2 ð17Þ

Fig. 7 Proposed adaptive neuro-fuzzy network structure


Each node in the second layer is a fixed type node denoted by “Π” and their output

generate the product (AND operator of fuzzy logic) of its inputs, that correspond to

the degree of the relevant rule membership, presented as follows:

Oi2 ¼ wi ¼ μAi xð Þ � μBi xð Þ ; i ¼ 1; 2 ð18Þ

Each node in third layer is a fixed type and it carries out the normalization of the

weights of the fuzzy rules, given by:

Oi3 ¼ wi ¼ wi

w1 þ w2; i ¼ 1; 2 ð19Þ

In the fourth layer, each node is adaptive and calculates the outputs of the rules by

performing the following function:

Oi4 ¼ wi � f i ¼ wi pixþ qiyþ rið Þ ; i ¼ 1; 2 ð20Þ

The fifth layer comprises a single neuron providing the output ANFIS by calculating

the sum of the outputs of the previous layer. Its output which is also presenting the

network output is determined by the following relationship:

Oi5 ¼ f ¼

Xi

wi � f i ð21Þ

On the other side, the ANFIS system learning is made from a set of data identifica-

tion of the premises and consequences parameters of the fuzzy system, where the

network structure is fixed. To achieve this phase of ANFIS system learning, a gradient

descent algorithm with a least squares estimation using a hybrid learning rule is

proposed as shown in Table 3. Hence, the following expression is obtained:

f ¼ W 1f 1 þW 2f 2 ð22Þ

Table 3 The two passes in the hybrid learning

Forward Pass Backward Pass

Premise Parameters Fixed Back-propagation

Consequent Parameters Least Squares Estimate fixed

Table 4 Dynamic model parameters

Parameter Value

Radiation shield parameter GSH 0.85

Radiation shield time constant TSH 12.2 s

Exhaust Temperature 537.3 °C

Fuel Demand signal Max Limit (maxKNL) 1.5

Fuel Demand signal Min Limit (minKNL) -1

K1 = 25; K2 = K3 = 1

F1 : TR−D 1− _mfP⋅Uð Þ þ 0:6 1−Nð ÞF2 : Aþ B⋅ _mfp⋅u þ 0:5 1−Nð ÞTI = 15.64


Withf 1 ¼ p1x1 þ q1x2 þ r

f 2 ¼ p2x1 þ q2x2 þ r2

�and with a linear combination of consistent modifi-

able parameters {p1, q1, r1, p2, q2, r2}.Consequently:

f ¼ W 1x1� �

:p1 þ W 1x2� �

:q1 þW 1:r1 þ W 2x1� �

:p2 þ W 2x2� �

:q2 þW 2:r2 ð23Þ

Note that in this algorithm parameters corresponding both to the premises and of

the consequences are optimized.

Discussion and evaluationThis work proposes the application of a hybrid approach based on an adaptive neuro-

fuzzy inference system “ANFIS” to ensure the speed and the exhaust temperature

control of a gas turbine 7001B. The control of the gas turbine system is performed in a

closed loop, where the control type is isochronous, the system output and input data

under normal operating conditions are used per unit of speed and temperature is based

00.2

0.40.6

0.81 100

200300

400500

−20

−15

−10

−5

0

5

input2input1

outp

ut

Fig. 8 Output area of the used ANFIS model

Time(s)0 5 10 15 20

Spe

ed (

p.u)

0

0.2

0.4

0.6

0.8

1

1.2Reference speedOptimized speed

9.65 9.7 9.75 9.8

0.999

1

1.001

Fig. 9 Speed variation using Rowen model


on the Rowen model for the HDGT we study. The parameters of this model are given

in Table 4.

The following expressions are used to calculate the exhaust temperature and the

torque of the gas turbine respectively:

F1 : TR−D 1− _mfP⋅U� �þ 0:6 1−Nð Þ

F2 : Aþ B⋅ _mfp⋅u þ 0:5 1−Nð Þ

The proposed based ANFIS controller has two inputs (Tx error, error n) and one out-

put. Each input has three fuzzified fuzzy set of Gaussian types. Figure 8 shows the

ANFIS controller surface of the studied gas turbine variables.

The obtained results of the high-efficiency gas turbine system control during startup

using the ANFIS approach are shown in Figs. 9 and 10. Figure 9 shows the speed

Time (s)0 10 20 30 40 50 60 70 80 90

Exh

aust

Tem

pera

ture

( °

C)

0

100

200

300

400

500

600

Fig. 10 Exhaust temperature variation using Rowen model

Time (s)0 10 20 30 40 50 60 70 80 90

Exh

aust

Tem

pera

ture

( °

C)

0

100

200

300

400

500

600

700

PID

ANFIS

Fig. 11 Exhaust temperature response comparison between ANFIS and PID controllers


variation per unit based on the Rowen model and Fig. 10 shows the measured exhaust

temperature variation of the studied gas turbine.

In order to validate the ANFIS approach, a comparison with a PID controller has

been performed for the both responses of the exhaust temperature and the speed as

shown in Figs. 11 and 12 respectively. It is clear to see that, in both responses, the

ANFIS controller responds more rapidly than does the PID controller. Indeed, the time

response of the ANFIs is 5 s, whereas the time response of the PID is 34 s, which

means that a gain of 29 s can be ensured by the use of the ANSIS controller. On the

other hand the peak presented in the response of the PID controller response is

avoided totally with the ANFIS controller. Therefore the ANFIS controller is more effi-

cient and obtains a faster response time than the PID controller, suggesting that a re-

duced cost may be obtained when the ANFIS controller is used for complex industrial

gas turbine systems.

Time (s)80 90 100 110 120 130 140 150 160 170

Mec

hani

cal P

ower

(u.

p)

0.7

0.8

0.9

1

1.1

1.2

1.3

135 145 155

1.1

1.15

1.2

Reference speed

optimized speed

For 15 % droop

Fig. 13 Speed variation using Rowen model after speed step 15%

Time(s)0 10 20 30 40 50 60 70 80

Spe

ed (

p.u)

0

0.2

0.4

0.6

0.8

1

1.2

40 50

0.98

1

PID

ANFIS

Fig. 12 Speed response comparison between ANFIS and PID controllers


This improved response is achieved by imposing the desired performance by limiting

the maximum overshoot value at around 15% as shown in Figs. 13 and 14. From these

two figures, it can be noted clearly that the proposed controller is working accurately

with acceptable performances.

The results obtained in Figs. 13 and 14 describe the control of HDGT of 157.7 MW

type of the 7001B turbine model, based on the ANFIS approach using the Rowen

model parameters reported in [18]. These results clearly show that the ANFIS control-

ler achieves better performance for the control of the speed and the exhaust

temperature of the gas turbine studies, which allows an efficiency improvement to be

obtained for the entire system.

For this particular gas turbine, to understand the effect of the speed control on other

gas turbine system parameters, simulations have been performed based on the use of a

PID controlled to ensure the control of a Model 7001B gas turbine used in the present

study. Indeed, two tests have been achieved using two distinct sets of PID controller

parameters in order to check their impact on the controlled gas turbine parameters.

Time (s)0 50 100 150 200 250 300

Spe

ed N

(p.

u)

0

0.2

0.4

0.6

0.8

1

1.2

1.4The speed of rotation

Reference speedSpeed after regulation

Load disturbance

Fig. 15 Rotation speed of the gas turbine

Time (s)100 120 140 160 180 200

Exh

aust

Tem

pera

ture

(°C

)

508

508.5

509

509.5

510

510.5

511

511.5

512

for 15% droop

Fig. 14 Exhaust temperature after speed step 15%


The simulations results so obtained are shown in Figs. 15, 16, and 17 for test 01 and in

Figs. 18, 19 and 20 for test 02.

In this case, two factors affect the speed and the exhaust temperature of the gas tur-

bine, as depicted in Figs. 15 and 16. The first factor is the change of the load at

t = 100 s and the second factor is the linear decrease of the reference speed beginning

at t = 130 s and ending t = 200 s. Figure 15 makes it clear that the PID controller can

control the gas turbine speed only very slowly at the initial transient peak around

t = 30s when it returns to its reference value.

First test 01: Kp = 10.5, Ki = 1, Kd = 1

In this case, there are two factors that are affecting the speed and the exhaust

temperature of the gas turbine as shown in Figs. 15 and 16, the first factor is the

change of the load at t ¼ 100s, and the second factor is the linear decrease of the

reference speed during the interval of [130s 200s]. it can be seen clearly that the

Time (s)0 50 100 150 200 250 300

Tx

(°F

)

0

200

400

600

800

1000

1200

1400Tha exhaust temperature

Without PID contrellerWith PID contrellerExceeds 950 ° F

Fig. 16 Exhaust temperature of the gas turbine

Time (s)0 50 100 150 200 250 300

Tor

que

(p.u

)

-0.5

0

0.5

1

1.5

2 The turbine Torque

Exceeds 1.5 per

Fig. 17 Gas turbine torque


PID controller can achieve the control of the gas turbine speed but slowly along

30swhere it rejoins its reference value (1 p.u). Furthermore, Fig. 15 shows that an

important overshoot of the developed speed over the reference speed, implying that

control is not accurate. At the same time the control of the exhaust temperature is

not accurately achieved, as an overshoot above the allowed limit of 960F is

observed. Such temperature overshoots can damage the turbine if they last for

more than a very short time. However there is a difference between the responses

with and without the use of the PID controller, as shown in Fig. 16. Figure 16

shows that the PID controller is not doing a good job, due to a poor choice of

PID controller constants. The same outcome is visible in Fig. 17 for torque behav-

ior dynamics. The perturbation in the system set point at t = 100 is caused by the

external operating constraints and between t = 130 and t = 200 is due to the air

leakage at the compressor level.

Time (s)0 50 100 150 200 250 300

Spe

ed N

(p.

u)

0

0.2

0.4

0.6

0.8

1

1.2



Load disturbance


Time (s)0 50 100 150 200 250 300

Tx

(°F

)

0

200

400

600

800

1000


Without PID controllerWith PID controller

Exceeds 960 ° F



On the other hand, for a linear decrease in reference speed, the PID controller

has better dynamics. It can be said the control is achieved and these results can

be accepted as shown in Figs. 15, 16, and 17. For the start up operation of the

turbine the PID controller has very results and presents a real danger as shown in

Figs. 15, 16 and 17. Again, this can be explained by poor choice of PID

parameters.

Second test 02: Kp = 10.5, Ki = 0, Kd = 0

In this case, the same factors as in the first case are presented. Here there is a change

of the load at t = 100 s and a linear decrease of the reference speed during the time

interval [130 s, 200 s]. Figure 18 makes it clear that the controller under the new pa-

rameters has achieved better control of the gas turbine speed when it rejoins its refer-

ence value of 1 per unit at t = 100 s. At the same time the gas turbine develops excess

torque to compensate for the drop in developed speed and to ensure stable operation

Time (s)0 50 100 150 200 250 300

Tor

que

(p.u

)

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4The turbine torque


Time (s)0 50 100 150 200 250 300

Spe

ed N

(p.

u)

0

0.2

0.4

0.6

0.8

1

1.2



Load disturbance



at the reference speed, as shown in Fig. 20. However, due to this sudden perturbation

the exhaust temperature rapidly increases as shown in Fig. 19. To clarify the main role

of the controller used here, the exhaust temperature dynamics are presented both with

and without the use of the controller. It can be seen that the difference is very clear

when the exhaust temperature increases sharply. Indeed, the level exceeds the max-

imum allowed level of 960F very rapidly to reach a huge overshoot value of 1160F

which can cause damage to the whole system. However, when the controller is used,

the increase in exhaust temperature happens only slowly and never reaches the max-

imum limit, for a safe operation.

The second factor here is the linear speed decrease after t = 130 s representing a very

soft braking of the turbine. In this case Figs. 18 and 19 show that the controller can

achieve a very accurate and rapidly compensating control of both speed and exhaust

temperature. At the same time, as shown in Fig. 20, torque dynamics respond well. We

can conclude that, as long as the choice of PID parameters remains adequately accurate

things are well even though the PID controller cannot support the proper control of

gas turbine parameters over a wide range.

Time (s)0 50 100 150 200 250 300

#Tor

que

(p.u

)

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2The turbine Torque

Value of the pick turbinedoes not exceed 1.5 per


Time (s)0 50 100 150 200 250 300

Tx

(°F

)

0

100

200

300

400

500

600

700

800

900


Without ANFIS controllerWith ANFIS controller

Pick exhaust temperaturedoes not exceed 950 ° F



The proposed controller based on ANFIS approach

In order to validate the improved performance of the proposed ANFIS controller over the

classical PID control, we performed a simulation test in which the same conditions as de-

scribed in Section 3.2 were implemented. The results obtained are presented in Figs. 21,

22, and 23. It can be seen that the ANFIS controller eliminates the three parametric peaks

in the speed, the exhaust temperature, and the torque at start-up, avoiding the turbine

damage risk associated with the classical PID controller. In particular, Fig. 23 shows a re-

duction in torque peak to about half the previous PID peak. On the other hand, controller

performance in ensuring stability of speed and exhaust temperature is very satisfactory in

that disturbances caused either by load change or speed reference change are rapidly

brought under control, allowing speed and exhaust temperatures to rejoin their respective

references within a very short time without substantial overshoot values. In conclusion,

we can report that the proposed ANFIS controller allows the smooth control of complex

gas turbines even under parameter variation.

ConclusionThe present work deals with the use of a Neuro-Fuzzy Adaptive Inference System

(ANFIS) controller designed to ensure adequate control of speed and exhaust

temperature for a Heavy Duty Gas Turbine (HDGT) based on Rowen Model equations.

The results obtained clearly demonstrate the high performance of the proposed con-

troller and its validity under varying operating conditions, in particular in comparison

with the classical PID controller. In future work, more control parameters and different

new constraints could be added to the study.

AcknowledgementsWe would like to express our gratitude and acknowledgements to the staff of the Applied Automation and IndustrialDiagnostics Laboratory of the University of Djelfa for his endless guidance and encouragement during the realizationof this work.

FundingThis work is carried out by the Automation and Industrial Diagnostics Laboratory of the University of Djelfa, Algeria.

Authors’ contributionsAll authors read and approved the final manuscript.

Competing interestsThis work proposes the integration of the artificial intelligence tools based on adaptive neuro-fuzzy inference systemto ensure the heavy duty gas turbine monitoring, this fuzzy approach has the advantage of no need to the use of theanalytical models to control the speed and the exhaust temperature in this equipment and make the gas turbineperformance monitoring improved. This fuzzy method proposed in this paper permits based on the obtained gasturbine data to obtain information on system status, which will be useful for real time supervision.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details1Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa,17000 Djelfa, DZ, Algeria. 2Faculty of Science and Technology, University of Médéa, 26000 Médéa, Algeria.3Aeronautical Aerospace Automotive Railway Engineering School, ESTACA, Paris, France.

Received: 2 July 2016 Accepted: 5 October 2017

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